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## BLIS/Swayable: Figure 4 ##
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# File Description: 
# Uses data/packages from O_Data.R
# Replicates Figure 4 from Fabric of Repair report (2025)
# Date Last Updated: 2 May 2025

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# Regression model for Land Back support
land_back_overall <- lm(Land.Back.Support ~ treatment + video_perception, df)

# Regression model for Reparations support
reparations_overall <- lm(Reparations.Support ~ treatment + video_perception, df)

# Making dataframe of regression coefficients for Land Back model
tidy_land_back <- tidy(land_back_overall) %>%
  mutate(model = "Land Back Support")

# Making dataframe of regression coefficients for Reparations model
tidy_reparations <- tidy(reparations_overall) %>%
  mutate(model = "Reparations Support")

# Binding regression coefficient dataframes together
both_models <- bind_rows(tidy_land_back, tidy_reparations) %>%
  relabel_predictors(c("treatmentReparations" = "Reparations Video",
                       "treatmentLand Back" = "Land Back Video",
                       "treatmentBoth" = "Both Video")) %>%
  filter(term != "video_perception")

# Plot Figure 4 from regression coefficients
dwplot(both_models, 
                # here are our regular aesthetics
                dot_args = list(aes(colour = model)), 
                size = 3) + 
  theme_bw() + 
  labs(title = "Persuasion Effects of Garrison's Videos", subtitle = "Referenced Against Control Video", 
       y = "", caption = "Model includes a control for respondents' perceived production quality.") +
  scale_x_continuous("Coefficient Estimate with 95% CIs") +
  geom_vline(xintercept = 0, colour="grey", linetype = "longdash") +
  scale_colour_manual(values = c("#1DA595", "#F94B01")) # start/end for light/dark greys
